RESEARCH AND PRACTICE
Dental Care Coverage and Use: Modeling Limitations and Opportunities Originally published as: Richard J. Manski, DDS, MBA, PhD, John F. Moeller, PhD, and Haiyan Chen, MD, PhD. Dental Care Coverage and Use: Modeling Limitations and Opportunities. Am J Public Health. 2014;104:e80–e87. doi:10.2105/AJPH.2013.301693.
According to 1 report, about 48% of adults with any private health insurance during 2000 had at least 1 dental visit, compared with about 29% of those with public health insurance and only 19% of those who were uninsured for the full year.1 The health insurance and dental care use association appears weak until one realizes that the insurance in question is actually medical insurance and not dental insurance. So, in fact, the reported rate is below that of other findings, which show that about 56% of adults with any private dental insurance had at least 1 dental visit, compared with about 22% of those who were uninsured for the full year.2 That there is any relationship between having medical insurance and seeking dental care at all is surprising because few medical insurance plans cover dental care services.3 Because medical insurance does not usually provide reimbursement for dental care, the medical insurance variable must represent some non---insurance-related unobserved health-seeking behavior that results in increased dental care use. However, several studies have examined the relationship between dental insurance coverage and dental care use, controlling for numerous socioeconomic and demographic variables.4---12 These studies show that, as expected, dental insurance is an important factor in the decision to seek and use dental services. Although the role of dental insurance coverage as a determinant of dental care use is now well established, less is known about the magnitude of its effect or how its effect may be modulated by other observed or unobserved factors. In 1 study, analysts measured the extent of the dental care coverage effect by analyzing Medical Expenditure Panel Survey data. Their results showed that the effect of dental care coverage is significant and increases the likelihood of a dental visit by 13%.13 A study examining Health and Retirement Study (HRS) data found that providing universal
Objectives. We examined why older US adults without dental care coverage and use would have lower use rates if offered coverage than do those who currently have coverage. Methods. We used data from the 2008 Health and Retirement Study to estimate a multinomial logistic model to analyze the influence of personal characteristics in the grouping of older US adults into those with and those without dental care coverage and dental care use. Results. Compared with persons with no coverage and no dental care use, users of dental care with coverage were more likely to be younger, female, wealthier, college graduates, married, in excellent or very good health, and not missing all their permanent teeth. Conclusions. Providing dental care coverage to uninsured older US adults without use will not necessarily result in use rates similar to those with prior coverage and use. We have offered a model using modifiable factors that may help policy planners facilitate programs to increase dental care coverage uptake and use. (Am J Public Health. 2014;104:2002–2009. doi:10.2105/AJPH.2013.301693)
dental care coverage for an older US population would increase dental care use only 1% to 8% after applying a nonparametric approach to account for errors in measuring self-reported dental care coverage and unobserved factors for aversion to risk and future dental care needs (B. Kreider, J. Pepper, R. Manski, and J. Moeller, unpublished data, 2012). In this study the increase in dental care use was significant but not as large or far-reaching as initially expected. Although the analyses confirmed that dental care coverage increases the likelihood of dental care use, the results also suggested that the effect of providing dental care coverage on use may be surprisingly lower than expected. We have provided empirical evidence and a theoretical model to help explain why the effect of dental care coverage on use may be less than expected and to more fully describe dental care use in relation to dental care coverage and other relevant determinants of use. Our findings may help research analysts, program developers, and policy planners better understand problems associated with policies and programs designed to encourage greater use of dental care among the population.
2002 | Research and Practice | Peer Reviewed | Manski et al.
METHODS We have proposed a comprehensive diagrammatic model (Figure 1) of the relationship between dental care use and multiple factors that are either modifiable or nonmodifiable for program planning or policy development.14 To develop our model we first began with and then adapted Andersen’s behavioral model of health services use as a theoretical framework to guide the development.15 Specifically, we considered several predisposing factors (age, gender, education, employment status, marital status, race/ethnicity, and health attitudes), enabling factors (income level and dental care coverage), and need factors (self-reported health status).15 We then extended our model to include market supply variables, environmental factors, and biomedical factors.16,17 We then made additional changes to our model to correspond with factor type and degree to which programs or policies could be developed to make an impact. For instance, we assembled factors into 2 broad groups: modifiable and nonmodifiable. Age, gender, and race/ethnicity are examples of nonmodifiable
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FIGURE 1—Comprehensive model of health services use: Health and Retirement Study, 2006–2008.
factors, and dental care coverage is an example of a modifiable factor because persons without coverage can receive coverage as a consequence of a new program or policy or can buy it on the market. Nonmodifiable factors are useful and important for purposes of program targeting and planning. We assembled all modifiable factors into groups to distinguish between preference, enabling, predisposing, need, market supply, environmental, and biomedical factors. Preference factors include knowledge, perception, tastes, attitudes, and beliefs. Enabling and predisposing factors include income, wealth, education, marital status, and family size. Need factors include overall health and oral health status. Market supply factors include numbers of dentists and dental hygienists.17 Less is known about environmental and biomedical factors, but we include
these in our model as a placeholder to guide future research.16 We used this reconfigured and expanded theoretical model to develop our regression models subject to data availability constraints in the HRS.
Data The HRS is a nationally representative longitudinal household survey administered by the Institute for Social Research at the University of Michigan and sponsored by the National Institute on Aging. It collects data every 2 years for individuals older than 50 years and their spouses.18,19 Specifically, we used the 2008 wave of the HRS, which contained 17 217 surveyed persons. The HRS is useful for the study of aging, retirement, and health among older US populations. It contains detailed information about
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demographics; income and assets; physical and mental health; cognition, family structure, and social supports; health care use and costs; health insurance coverage; labor force status and job history; and retirement planning and expectations. Each wave of the HRS collects self-reported information about medical and dental care use since the previous survey wave, approximately 2 years earlier.18,19 The HRS provides detailed information on numerous factors associated with dental care coverage and use, including out of pocket payments for dental visits. From the 2008 HRS sample of 17 217, we excluded 4250 respondents. Of these, we estimated that 1570 would not qualify for expanded Medicaid dental care coverage because they were aged 55 to 64 years, not on Medicare, and living in households with
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RESEARCH AND PRACTICE
income greater than 133% of the poverty threshold. We used the income cutoff to identify Medicaid (below the cutoff) and Medicare (above the cutoff) recipients of expanded dental care coverage for those uninsured aged 55 years and older. We omitted another 1875 respondents because they had zero weights, as did the 166 who were younger than 55 years, an age group underrepresented in the HRS. The other 639 respondents we omitted represented less than 4% of the 2008 HRS sample and had missing data on at least 1 of the independent variables in our analysis. We then split our final sample of 12 967 respondents into 4 groups on the basis of their dental care coverage and dental care use as reported in the HRS and as illustrated in Table 1. Groups 1 and 2 had dental care coverage but only group 1 had dental care use. Groups 3 and 4 did not have dental care coverage, and only group 3 had dental care use.
health (self-reported health status, body mass index [BMI; defined as weight in kilograms divided by the square of height in meters], number of limitations in specific activities of daily living [ADLs], and number of chronic conditions ever diagnosed by a physician), and oral health status (all permanent teeth missing or not); and (2) nonmodifiable variables, including age, gender, and racial/ ethnic background. All independent variables are categorical except the numbers of ADLs and chronic conditions, which were continuous. We first estimated the relationship between group membership and each independent variable without adjusting for other covariates. Next, we estimated 3 multinomial logistic regressions for group membership distinguished by altering the reference groups for the regressions between groups 1 to 3 to produce the odds ratios (ORs) for the independent variables adjusted for other covariates in the models. This produced 6 unique comparisons: group 1 with each of the other 3 groups, group 2 with groups 3 and 4, and group 3 with group 4. We focused on comparing characteristics of persons with and those without dental care use holding dental care coverage constant, so we have shown only partial results from the multinomial logistic regressions comparing groups 1 and 2 (both with dental care coverage) and groups 3 and 4 (both without dental care coverage). In addition we have shown partial multinomial logistic regression results comparing persons in group 1 (those with both dental care coverage and use) to persons in group 4 (those lacking both coverage and use). The remaining 3 multinomial logistic results mainly contrast characteristics of those with and those without dental care coverage, holding dental care use constant (data available on request).
Analysis Our study was motivated by the relatively low dental care use rates estimated for persons without dental care coverage and use (group 4) under a hypothetical scenario in which they were assumed to have dental care coverage.14 This suggested that having dental care coverage per se was not sufficient to encourage older persons to use dental services at the same rate as those previously covered. This raised the question of what personal characteristics distinguished each of the 4 groups. To address this, the dependent variable in our study is group membership defined by the presence or absence of dental care use and dental care coverage. The independent variables included (1) modifiable variables, including household income relative to the poverty line, household wealth, education, marital status, region, retirement and labor force status, household size,
TABLE 1—Dental Insurance and Dental Care Use for Community-Based US Adults Aged 55 Years and Older: Health and Retirement Study, 2006–2008 Dental Coverage, No. Total Population, No.
Dental Use (Group 1)
No Dental Use (Group 2)
No Dental Coverage, No. Dental Use (Group 3)
No Dental Use (Group 4)
Sample Size
12 967
4337
1695
3809
3126
Weighted Sample (1000s)
60 370
25 188
7737
15 127
12 319
2004 | Research and Practice | Peer Reviewed | Manski et al.
The HRS core sample design is a multistage area probability sample of households, so we computed all estimates and statistics we have reported taking into account this design with the use of the software packages SUDAAN and Stata.20,21 We have discussed only estimates that were statistically significant at least at the 5% level.
RESULTS As shown in Table 2, group 1 respondents (those with dental care coverage and dental care use) were generally more likely than were each of the other 3 groups to be younger, college graduates, married, in excellent or very good health, not missing all their permanent teeth, high income, and in the labor force and not retired, and they were more likely to have fewer difficulties with ADLs and chronic conditions. They were also more likely than were persons without dental care use, whether with (group 2) or without (group 4) coverage, to be White non-Hispanic, living in households of 2 persons, in the highest household wealth decile, and living in the Midwestern or southern but not the western regions of the United States.
Persons With Dental Care Coverage and No Dental Care Use Group 2 respondents (those with dental care coverage but no use) were more likely than were groups 3 and 4 (those without any dental care coverage) to be younger, Black nonHispanics, living in a household with 3 or more persons and not alone, obese, and in the labor force and not retired. They were also more likely than were those in group 3 (persons without coverage but with use) to be male, Hispanic, high school but not college graduates, not in a 2-person household, in fair or poor health, missing all permanent teeth, poor and not high income, in the lowest wealth deciles, and not partly retired, and they were more likely to have a greater number of difficulties with ADLs. They were also more likely than were persons with no coverage and no dental care use (group 4) to be college graduates and not high school graduates, married, in excellent or very good health, not missing all their permanent teeth, high income and not poor or low income, and not in the labor force and not retired, and
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TABLE 2—Population Characteristics of Groups of US Civilian, Noninstitutionalized Persons Defined by Dental Care Coverage and Dental Care Use: Health and Retirement Study, 2006–2008 Dental Care Coverage
Population Characteristics
No Dental Care Coverage
Population Size (1000s)
Total (n = 60 370), Mean (SD)
Group 1: Dental Care Use (n = 25 188), Mean (SD)
Group 2: No Dental Care Use (n = 7737), Mean (SD)
Group 3: Dental Care Use (n = 15 127), Mean (SD)
Group 4: No Dental Care Use (n = 12 319), Mean (SD)
23 304
38.15 (0.67)
63.20e (1.06)
48.19f (1.83)
7.44g (0.76)
18.36 (1.26)
e
f
Age, y 55–64 65–69
11 737
19.44 (0.45)
15.40 (0.63)
17.63 (1.17)
25.20g (1.03)
21.76 (1.00)
70–74
9493
15.73 (0.31)
9.42e (0.46)
13.61f (0.78)
23.74g (0.77)
20.09 (0.76)
75–79
6864
11.37 (0.33)
5.91e (0.35)
9.00f (0.64)
18.71g (0.58)
15.00 (0.68)
80–84
4612
7.64 (0.35)
3.39e (0.17)
6.16f (0.49)
12.37 (0.70)
11.44 (0.76)
4630 33 702
7.67 (0.34) 55.83 (0.46)
2.67e (0.25) 55.53d (0.71)
5.40f (0.64) 53.02d (1.76)
12.53 (0.76) 58.37g (0.82)
13.34 (0.76) 55.07 (0.92)
5507
9.12 (0.48)
7.89e (0.65)
19.38f (1.14)
3.68g (0.33)
11.88 (0.85)
‡ 85 Gender: female Race Black non-Hispanic
3769
6.24 (0.78)
5.30 (0.50)
9.86 (1.50)
4.06g (0.59)
8.59 (1.75)
White non-Hispanic
49 659
82.26 (0.96)
84.24e (0.87)
66.99f (2.03)
90.94g (0.68)
77.14 (1.87)
Other non-Hispanic
1435
2.38 (0.32)
2.58d (0.34)
3.77 (1.09)
1.32g (0.22)
2.40 (0.47)
Some or no school High school graduate
35 301 10 544
58.47 (0.75) 17.47 (0.67)
57.47d (1.09) 7.04e (0.40)
57.47 (1.91) 30.53f (1.77)
61.49g (0.99) 13.58g (0.62)
57.46 (1.38) 35.35 (1.42)
College graduate
14 525
24.06 (0.84)
35.49e (1.23)
12.00f (1.16)
24.94g (1.12)
7.19 (0.63)
37 158
61.55 (0.73)
71.17e (0.86)
55.56g (1.86)
59.36g (1.08)
48.34 (1.28)
2344
3.88 (0.22)
4.02 (0.32)
4.52 (0.65)
3.60 (0.40)
3.55 (0.43)
20 868
34.57 (0.67)
24.81e (0.83)
39.92g (1.60)
37.05g (0.99)
48.11 (1.17)
1 2
15 889 32 042
26.32 (0.51) 53.08 (0.80)
20.59e (0.66) 55.94c (1.11)
25.07f (1.32) 44.76d (1.59)
30.95 (0.98) 57.54g (1.12)
33.13 (1.28) 46.96 (1.56)
‡3
12 440
20.61 (0.69)
23.47e (0.91)
30.17f (1.83)
11.50g (0.83)
19.91 (1.10)
24 190
40.07 (0.76)
50.68e (0.99)
31.05f (1.33)
40.59g (1.13)
23.41 (1.00)
Hispanic
c
d
Education
Marital status Married Widowed or divorced Never married Household size, No. persons
Health status Excellent or very good Good
19 167
31.75 (0.65)
31.64 (0.95)
29.55 (1.28)
34.46g (0.95)
30.02 (1.33)
Fair or poor
17 014
28.18 (0.66)
17.68e (0.64)
39.39f (1.51)
24.95g (0.70)
46.57 (1.22)
9749
16.15 (0.51)
3.00e (0.26)
35.61f (1.49)
5.88g (0.37)
43.41 (1.17)
978
1.62 (0.52)
0.87f (0.21)
1.42 (0.42)
2.45 (0.32)
2.27 (0.35)
Normal
17 300
28.66 (0.52)
26.91e (0.72)
22.41f (1.25)
34.14g (1.05)
29.42 (0.94)
Overweight
23 100
38.26 (0.50)
39.46g (0.86)
36.36 (1.54)
38.84 (0.99)
36.30 (1.06)
Obese
18 992
31.46 (0.58)
32.76b (0.82)
39.81f (1.42)
24.58g (0.97)
32.00 (1.04)
5676
9.40 (0.57)
4.53e (0.41)
14.66f (1.54)
6.96g (0.62)
19.07 (1.30)
All permanent teeth missing Body mass index Underweight
d
d
Family incomea Poor Low Middle High
e
g
9977
16.53 (0.41)
7.47 (0.50)
20.15 (1.09)
19.51g (0.86)
29.10 (1.04)
17 321 27 396
28.69 (0.55) 45.38 (0.95)
23.41e (0.84) 64.59e (1.25)
31.49 (1.40) 33.70f (1.75)
33.40 (0.82) 40.13g (1.25)
31.94 (1.01) 19.89 (0.88) Continued
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RESEARCH AND PRACTICE
TABLE 2—Continued Household wealth, decile 1–3
16 576
27.46 (0.86)
19.11e (0.92)
48.45d (1.72)
16.23g (0.85)
45.12 (1.42)
d
4–6
18 431
30.53 (0.73)
30.44 (1.16)
32.42 (1.37)
28.23g (1.31)
32.35 (1.04)
7–9
19 330
32.02 (0.99)
39.05c (1.33)
16.39d (1.24)
39.26g (1.32)
18.57 (1.18)
6034
9.99 (0.52)
11.40e (0.74)
2.74d (0.43)
16.29g (1.01)
3.95 (0.45)
15 758
26.10 (1.66)
27.85c (2.17)
21.95d (1.97)
28.83g (2.42)
21.79 (1.62)
Northeast
10 525
17.43 (1.42)
17.87 (1.68)
16.23 (1.53)
17.79 (1.84)
16.82 (1.93)
South
11 960
19.81 (1.61)
23.06c (2.07)
17.10 (1.71)
19.74 (1.84)
14.97 (1.69)
West
22 128
36.65 (1.41)
31.21c (1.61)
44.72d (1.95)
33.64g (2.02)
46.42 (2.14)
31 755
52.60 (0.73)
35.83e (1.04)
49.92f (1.59)
68.25 (1.02)
69.35 (1.31)
6182
10.24 (0.31)
e
10.38 (0.51)
7.79d (0.73)
12.94g (0.73)
8.18 (0.68)
16 099 6335
26.67 (0.66) 10.49 (0.41)
45.87e (1.31) 7.92e (0.51)
30.89f (1.88) 11.41g (0.96)
7.95 (0.55) 10.86g (0.62)
7.74 (0.71) 14.73 (0.84)
NA
0.31 (0.01)
0.18e (0.01)
0.42f (0.03)
0.31g (0.02)
0.54 (0.02)
2.39g (0.03)
2.77 (0.04)
10 Region Midwest
Retirement or labor force status Fully retired Partly retired In the labor force, not retired Not in the labor force, not retired No. of difficulties with activities of daily living No. of chronic conditions
NA
2.27 (0.02)
e
1.89 (0.03)
f
2.47 (0.05)
Note. NA = not applicable. Table is derived from a sample of 12 967 persons from the 2008 Health and Retirement Study who were aged 55 years and older with positive-valued weights and without any missing data. Rounding accounts for any column sums not equal to totals. a Where low income refers to persons in families with incomes 101%–199% of the poverty line; middle income, 201%–400% of the poverty line; and high income, > 400% of the poverty line. Poor persons are at or below 100% of the poverty line, including persons in families with negative income. b Indicates that the mean in the column is significantly different from the mean of those with dental care coverage and no dental care use and the mean of those with dental care use and no dental care coverage (P £ .05). c Indicates that the mean in the column is significantly different from the mean of those with dental care coverage and no dental care use and the mean of those with no dental care coverage and no dental care use (P £ .05). d Indicates that the mean in the column is significantly different from the mean of those with dental care use and no dental care coverage (P £ .05). e Indicates that the mean in the column is significantly different from the mean in each of the other 3 columns (P £.05). f Indicates that the mean in the column is significantly different from the mean in each of the last 2 columns (P £ .05). g Indicates that the mean in the column is significantly different from the mean in the last column (P £ .05).
they were more likely to have had fewer difficulties with ADLs and chronic conditions.
Persons With Dental Care Use and No Dental Care Coverage Group 3 respondents (those with dental care use and no dental care coverage) were more likely than were group 4 respondents (those without dental care coverage and use) to be aged 65 to 79 years and not younger than 65 years; female; White non-Hispanic and not minorities; college graduates; married; living in households of 2 persons and not 3 persons or more; in good, very good, or excellent health; not missing all permanent teeth; normal BMI and not obese; high income and not poor or low income; in the higher wealth deciles; living in the Midwest and not in the western regions of the United States; and partly retired. They were also more likely to have fewer difficulties with ADLs and chronic conditions.
Dental Care Use As shown in Table 3, the odds of not using dental care relative to using dental care, holding coverage constant (group 2 vs group 1 and group 4 vs group 3), were higher for men than for women, Black non-Hispanics than White non-Hispanics, noncollege graduates than college graduates, never married than married, in fair or poor health than in excellent or very good health, missing all permanent teeth than not missing all permanent teeth, not high income, in the sixth or lower household wealth deciles than in the highest wealth decile, and living in the western than the southern region of the United States. For those with coverage (group 2 vs group 1), the likelihood of not using dental care was higher for those aged 65 years and older than for those younger than 65 years, for those in 3 or more person households than for those living alone, and for those obese than for those with normal BMI values.
2006 | Research and Practice | Peer Reviewed | Manski et al.
Interestingly, for those without coverage (group 4 vs group 3), the odds of not using dental care were lower for persons aged 65 years and older than for those younger than 65 years. In neither case was dental care use correlated with retirement and labor force status and the number of chronic conditions and difficulties with ADLs.
Dental Care Coverage and Use Respondents without coverage and use (group 4) have attributes highly correlated with nonuse relative to respondents with both (group 1), such as being much older, male, less educated, never married, less healthy, missing all permanent teeth, less income and wealth, and being in the West rather than the South (Table 2). White non-Hispanics have a higher likelihood than do Black non-Hispanics of being without dental care coverage and use than of having both. Similarly, persons with lower
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TABLE 3—Adjusted Odds Ratios from Multinomial Logistic Regressions Comparing US Participant Variables of Interest by Use and Insurance Coverage of Dental Care: Health and Retirement Study, 2006–2008 Group 2a vs Group 1,b OR (95% CI)c
Group 4d vs Group 3,e OR (95% CI)f
Group 4d vs Group 1,b OR (95% CI)c
55–64 (Ref) 65–69
1.00 1.51** (1.17, 1.95)
1.00 0.39** (0.28, 0.52)
1.00 4.64** (3.82, 5.62)
70–74
1.66** (1.30, 2.12)
0.35** (0.25, 0.47)
5.55** (4.49, 6.85)
75–79
1.64** (1.21, 2.23)
0.31** (0.23, 0.43)
5.72** (4.67 7.01)
80–84
1.84** (1.30, 2.59)
0.35** (0.25, 0.48)
6.70** (5.11, 8.79)
‡ 85
1.87** (1.24, 2.82)
0.37** (0.26, 0.53)
8.45** (6.11, 11.68)
1.56** (1.32, 1.85)
1.52** (1.31, 1.77)
1.69** (1.48, 1.93)
Population Characteristics Age, y
Gender Male Female (Ref) Race Black non-Hispanic
1.00
1.00
1.00
1.46** (1.14, 1.87)
1.72** (1.40, 2.12)
0.71** (0.57, 0.88)
Hispanic
0.95 (0.68, 1.34)
0.96 (0.67, 1.37)
0.70g (0.46, 1.06)
White non-Hispanic (Ref)
1.00
1.00
1.00
Other non-Hispanic
1.55 (0.90, 2.68)
1.50g (0.93, 2.42)
0.93 (0.59, 1.46)
Education Some or no school
1.59** (1.23, 2.05)
1.83** (1.54, 2.19)
2.31** (1.90, 2.81)
High school graduate College graduate (Ref)
2.89** (2.16, 3.86) 1.00
2.76** (2.22, 3.43) 1.00
3.54** (2.68, 4.67) 1.00
Marital status Married (Ref)
1.00
1.00
1.00
Widowed or divorced
1.52g (0.96, 2.40)
1.09 (0.69, 1.73)
1.42g (0.96, 2.10)
1.64** (1.20, 2.26)
1.46** (1.13, 1.89)
1.64* (1.26, 2.13)
Never married Household size, no. 1 (Ref) 2 ‡3
1.00 1.21 (0.85, 1.74) 1.36* (1.00, 1.86)
1.00
1.00
1.09 (0.81, 1.48) 1.36g (0.99, 1.85)
1.30g (0.98, 1.72) 1.14 (0.86, 1.52)
Health status Excellent or very good (Ref)
1.00
1.00
Good
1.01 (0.83, 1.24)
1.12 (0.95, 1.34)
1.33** (1.11, 1.60)
1.50** (1.26, 1.78)
1.59** (1.33, 1.91)
2.18** (1.86, 2.56)
11.60** (9.13, 14.73)
10.50** (9.06, 12.19)
10.38** (8.69, 12.41)
Fair or poor
1.00
Permanent teeth All missing Not all missing (Ref) Body mass index
1.00
1.00
Underweight
1.23 (0.57, 2.64)
0.61g (0.37, 1.02)
1.38 (0.73, 2.64)
Normal (Ref)
1.00
1.00
1.00
Overweight
1.19g (0.97, 1.41)
1.09 (0.93, 1.28)
1.08 (0.91, 1.27)
1.35** (1.10, 1.67)
1.16g (1.00, 1.36)
1.10 (0.94, 1.28)
Poor
2.29** (1.57, 3.32)
1.49* (1.02, 2.03)
6.02** (4.26, 8.51)
Low Middle
1.83** (1.39, 2.41) 1.35** (1.09, 1.67)
1.28* (1.04, 1.57) 1.26** (1.09, 1.47)
3.45** (2.68, 4.44) 1.91** (1.63, 2.23)
Obese
1.00
Household incomeh
High (Ref)
1.00
1.00
1.00 Continued
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numbers of chronic conditions are more likely than are those with higher numbers of being without dental care coverage and use than of having both. Being out of the labor force raises the likelihood of having neither use nor coverage compared with having both but only because coverage was not held constant in making this comparison. When coverage is controlled, there is no correlation between labor force and retirement status and dental care use.
DISCUSSION Policy planners and dental care advocates often look to users of dental care, and especially users of dental care with dental care coverage, to benchmark goals and objectives for programs designed to improve access among nonusers. Useful for establishing aspirational program targets, these goals and objectives are often not fully realized (B. Kreider, J. Pepper, R. Manski, and J. Moeller, unpublished data).13 Our results show that dental care users with dental care coverage differ from dental care nonusers without dental care coverage in ways that are important to consider when developing policy. More importantly, many of these differences persist even when controlling for dental care coverage. Persistence of nonuse, independent of dental care coverage status, suggests that attempts to improve access by focusing only on the provision of dental care coverage might be ineffective. Having established personal attributes correlated with dental care use better enables an understanding of why uninsured older US adults not using dental care are not likely to have high use rates even if they did obtain dental care coverage. The policy implications of these findings are important. Providing dental care coverage to an older population previously using dental care primarily reshuffles payment sources and may induce additional demand for services. More importantly, providing dental care coverage to an uninsured older population previously without dental care use may encourage them to seek dental services. Generally, a conventional understanding among health policy planners is that providing dental care coverage to this latter uninsured population would result in dental use estimates
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TABLE 3—Continued Household wealth, decile 1–3
4.21** (2.63, 6.74)
3.98** (2.99, 5.29)
2.54** (1.89, 3.40)
4–6
2.48** (1.64, 3.74)
2.38** (1.82, 3.10)
1.51** (1.12, 2.20)
7–9
1.29 (0.82, 2.20)
1.39* (1.03, 1.88)
10 (Ref)
1.00
1.00
1.00 1.09 (0.82, 1.45)
Region Midwest
0.90 (0.65, 1.25)
0.93 (0.71, 1.22)
0.81 (0.62, 1.05)
Northeast
1.06 (0.78, 1.45)
0.98 (0.77, 1.25)
1.17 (0.84, 1.64)
South (Ref)
1.00
1.00
1.00
West
1.47** (1.14, 1.90)
1.32** (1.08, 1.62)
2.05** (1.51, 2.78)
Retirement or labor force status Fully retired
0.97 (0.72, 1.30)
1.05 (0.80, 1.38)
2.30** (1.80, 2.94)
Partly retired
0.87 (0.63, 1.21)
0.88 (0.62, 1.24)
1.98** (1.42, 2.77)
In the labor force, not retired (Ref) Not in the labor force, not retired
1.00 0.78 (0.55, 1.09)
1.00 0.93 (0.70, 1.24)
1.00 2.14** (1.60, 2.85)
1.00 (0.90, 1.11)
0.99 (0.90, 1.08)
1.01 (0.93, 1.10)
0.96 (0.91, 1.02)
0.96 (0.91, 1.02)
0.93* (0.87, 0.99)
No. of difficulties with activities of daily living No. of chronic conditions
Note. CI = confidence interval; OR = odds ratio. The multinomial logistic regression contains a sample of 12 967 persons from the 2008 Health and Retirement Study who were aged 55 years and older, with positive-valued weights and without any missing data. For continuous covariates the OR point estimate is derived from a 1-unit change in the variable. The adjusted value refers to the inclusion in the regression of the control variables listed in the rows of the table. a Group 1 consists of dental care coverage and use. b Group 2 consists of dental care coverage and no dental care use. c Estimated from a multinomial logistic equation with group 1 (dental care coverage and use) as the reference group. d Group 4 consists of no dental care coverage and no dental care use. e Group 3 consists of dental care use and no dental care coverage. f Estimated from a multinomial logistic equation with group 3 (dental care use and no dental care coverage) as the reference group. g Approached statistical significance at P < .1. h Where low income refers to persons in families with incomes 101%–199% of the poverty line; middle income, 201%–400% of the poverty line; and high income, > 400% of the poverty line. Poor persons are at or below 100% of the poverty line, including persons in families with negative income. *P £ .05; **P £ .01.
similar if not identical to those of the previously covered population. Referred to by economists as the counterfactual, the group receiving a treatment is often different from the control group in many ways.22 Our targeted control group was persons with dental care coverage and dental care use. We targeted dental care coverage to uninsured persons without dental care use who have lower income, less wealth, lower educational levels, worse health, and higher age than does the targeted control group. It is likely that the targeted treatment group differs from the control group in attitude and preference for oral health. In fact, previous work by Cooper et al.13 showed that preference factors can and do modulate the effect on use of having coverage (B. Kreider, J. Pepper, R. Manski, and
J. Moeller, unpublished data). As such, social determinants of health should be included as a measure complementary to dental care coverage in efforts to improve dental care use in older US adults. In addition, if we assume that the dental care coverage and use market is stable and in economic equilibrium, providing dental care coverage to persons previously uninsured will disrupt that equilibrium and produce inefficiencies in achieving short-term marginal increases in use. Both demand-side and supply-side factors could result in less use than expected. For instance, expanded dental insurance might not increase use unless coverage reduced the cost of dental care to a level that was viewed as affordable. Also, if the dental care supply is fixed in the short term,
2008 | Research and Practice | Peer Reviewed | Manski et al.
appointments may be delayed or not available until capacity can be increased. Therefore, our findings are not surprising, and if the goal of providing coverage is to improve access among current nonusers, policy planners should contemplate including programs to increase the awareness and preference for dental care among nonusers and include programs to mitigate any disruption of the market equilibrium.
Model Application The value of this adaptation and approach is the capacity of this model to guide the development of new programs and policy. Accordingly, if the goal is to improve access by adding dental care coverage and thereby stimulate the use of dental services among current nonusers, policy planners should contemplate including programs to increase the overall acceptance, participation, and use of newly offered dental care coverage plans among eligible participants. Policy planners might wish to consider the addition of program elements designed to positively stimulate modifying factors by introducing a health promotion and literacy program that will improve the understanding of coverage and create a more positive outlook about dental care among modifiable preference variables such as knowledge, perception, tastes, attitudes, and beliefs. Furthermore, our findings support the inclusion of market supply factors in our model. For instance, the persistence of the western region influence showing lower use in that part of the country than other regions could proxy for market supply factors, such as a lower number of dentist or dental hygiene providers per capita. To mitigate any negative effect of supply-side variables, dental and dental hygiene school programs could be offered inducements to consider expanded and accelerated programs and debt forgiveness to provide workforce market stability during a period of increasing demand. Simply, recognizing the role of the supply-side variables and modifying factors makes the successful introduction of a plan to improve access and a plan that is more likely to approximate the prereform target goals more likely. Enabling and predisposing variables such as income, wealth, education, marital status, and family size are modifiable but only over
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a longer term. Policies and programs will take some time to implement and perhaps longer to have an effect. Understanding the implications of the time element during the program development could allow planners to better schedule, coordinate, and phase in new policies. Furthermore, our analyses showed that there is no correlation between employment status and dental care use when controlling for dental care coverage. Therefore, we treat coverage and employment status factors as interdependent, unique, and distinct from other enabling factors. In our model we posit that health and oral health status are also interdependent and are a function of preference variables, environmental variables, biomedical variables, and cointerdependent with use itself.16 Less is known about environmental and biomedical factors.16 Additional study and research is warranted to better understand the role and relationship of these factors, which may result in new opportunities to develop relevant and worthwhile interventions.
Conclusions Our findings show that the personal attributes of older US adults without dental care coverage and dental care use differ from those of older US adults with both use and coverage. This suggests that providing dental care coverage to uninsured older US adults without use will not necessarily result in higher use rates and likely will not result in use rates similar to those with prior coverage and use. We have offered a model and an approach showing how program developers and policy planners can consider modifiable factors as they plan for and attempt to implement dental care coverage extensions to persons previously uninsured. The use of this approach would make coverage uptake and use more likely and increase the likelihood that nonusers without coverage postextension would approximate or approach the use rates of those with prior use and coverage. j
About the Authors Richard J. Manski, John F. Moeller, and Haiyan Chen are with Dental Public Health, University of Maryland School of Dentistry, Baltimore. Correspondence should be sent to Richard J. Manski, DDS, MBA, PhD, Professor, Dental Public Health, University of Maryland School of Dentistry, 650 West Baltimore Street, Room 2209, Baltimore, MD 21201
(e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted September 20, 2013.
13. Cooper PF, Manski RJ, Pepper JV. The effect of dental insurance on dental care use and selection bias. Med Care. 2012;50(9):757---763.
Contributors
14. Manski RJ, Moeller JR, Chen H, et al. Dental care utilization and retirement. J Public Health Dent. 2010; 70(1):67---75.
R. J. Manski conceptualized and supervised the study and served as content expert on dental care. J. F. Moeller supervised the analyses and assisted with the writing of the article. H. Chen assisted with analyses.
Acknowledgments This investigation (Dental Coverage Transitions, Utilization and Retirement) was supported by the National Institute of Dental and Craniofacial Research of the National Institutes of Health (grant R01DE021678). The Health and Retirement Study is sponsored by the National Institute of Aging (grant NIA U01AG009740) and is conducted by the University of Michigan.
Human Participant Protection No protocol approval was necessary because public data were obtained from secondary sources.
15. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1---10. 16. Diez Roux AV. Integrating social and biologic factors in health research: a systems view. Ann Epidemiol. 2007;17(7):569---574. 17. Murphya GT, Kephart G, Lethbridgea L, O’BrienPallasc L, Birch S. Planning for what? Challenging the assumptions of health human resources planning. Health Policy. 2009;92(2---3):225---233. 18. RAND Center for the Study of Aging, Labor, and Population. RAND HRS Data, Version H. Santa Monica, CA; 2008. 19. St. Clair P, Blake D, Bugliari D. RAND HRS Data Documentation, Version J. Santa Monica, CA; 2010. 20. SUDAAN, Release 6.40. [computer program]. Research Triangle Park, NC: Research Triangle Institute; 1995.
References 1. Brown E, Manski R. Dental Services: Use, Expenses, and Sources of Payment, 1996---2000. Rockville, MD: Agency for Healthcare Research and Quality; 2004. AHRQ Pub. 04---0018. 2. Manski RJ, Brown E. Dental Use, Expenses, Private Dental Coverage, and Changes, 1996 and 2004. Rockville, MD: Agency for Healthcare Research and Quality; 2007.
21. Stata Statistical Software, Version 7.0. [computer program]. College Station, TX: StataCorp LP; 2001. 22. Manski CF. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press; 1995.
3. National Association of Dental Plans; Delta Dental Plans Association. Offering Dental Benefits in Health Exchanges: A Roadmap for Federal and State Policymakers. Dallas, TX: NADP/DDPA; 2011. NADP/DDPA white paper. 4. Manski RJ, Cooper PF. Dental care use: does dental insurance truly make a difference in the US? Community Dent Health. 2007;24(4):205---212. 5. Manning WG, Bailit HL, Benjamin B, Newhouse JP. The demand for dental care: evidence from a randomized trial in health insurance. J Am Dent Assoc. 1985; 110(6):895---902. 6. Manning WG, Phelps CE. The demand for dental care. Bell J Econ. 1979;10:503---525. 7. Mueller CD, Monheit AC. Insurance coverage and the demand for dental care. Results for non-aged White adults. J Health Econ. 1988;7(1):59---72. 8. US Department of Health and Human Services. Oral Health in America: A Report of the Surgeon General. Rockville, MD: National Institute of Dental and Craniofacial Research; 2000. 9. Institute of Medicine. Advancing Oral Health in America. Washington, DC: National Academies Press; 2011. 10. Institute of Medicine; National Research Council. Improving Access to Oral Health Care for Vulnerable and Underserved Populations. Washington, DC: National Academies Press; 2011. 11. US General Accounting Office. Factors Contributing to Low Use of Dental Services by Low-Income Populations. Washington, DC; 2000. GAO/HEHS-00---149. 12. US General Accounting Office. Dental Disease Is a Chronic Problem Among Low-Income Populations. Washington, DC; 2000. GAO/HEHS-00---72.
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Manski et al. | Peer Reviewed | Research and Practice | 2009
Dental Care Coverage and Use: Modeling Limitations and Opportunities Originally published as: Richard J. Manski, DDS, MBA, PhD, John F. Moeller, PhD, and Haiyan Chen, MD, PhD. Dental Care Coverage and Use: Modeling Limitations and Opportunities. Am J Public Health. 2014;104:e80–e87. doi:10.2105/AJPH.2013.301693.
According to 1 report, about 48% of adults with any private health insurance during 2000 had at least 1 dental visit, compared with about 29% of those with public health insurance and only 19% of those who were uninsured for the full year.1 The health insurance and dental care use association appears weak until one realizes that the insurance in question is actually medical insurance and not dental insurance. So, in fact, the reported rate is below that of other findings, which show that about 56% of adults with any private dental insurance had at least 1 dental visit, compared with about 22% of those who were uninsured for the full year.2 That there is any relationship between having medical insurance and seeking dental care at all is surprising because few medical insurance plans cover dental care services.3 Because medical insurance does not usually provide reimbursement for dental care, the medical insurance variable must represent some non---insurance-related unobserved health-seeking behavior that results in increased dental care use. However, several studies have examined the relationship between dental insurance coverage and dental care use, controlling for numerous socioeconomic and demographic variables.4---12 These studies show that, as expected, dental insurance is an important factor in the decision to seek and use dental services. Although the role of dental insurance coverage as a determinant of dental care use is now well established, less is known about the magnitude of its effect or how its effect may be modulated by other observed or unobserved factors. In 1 study, analysts measured the extent of the dental care coverage effect by analyzing Medical Expenditure Panel Survey data. Their results showed that the effect of dental care coverage is significant and increases the likelihood of a dental visit by 13%.13 A study examining Health and Retirement Study (HRS) data found that providing universal
Objectives. We examined why older US adults without dental care coverage and use would have lower use rates if offered coverage than do those who currently have coverage. Methods. We used data from the 2008 Health and Retirement Study to estimate a multinomial logistic model to analyze the influence of personal characteristics in the grouping of older US adults into those with and those without dental care coverage and dental care use. Results. Compared with persons with no coverage and no dental care use, users of dental care with coverage were more likely to be younger, female, wealthier, college graduates, married, in excellent or very good health, and not missing all their permanent teeth. Conclusions. Providing dental care coverage to uninsured older US adults without use will not necessarily result in use rates similar to those with prior coverage and use. We have offered a model using modifiable factors that may help policy planners facilitate programs to increase dental care coverage uptake and use. (Am J Public Health. 2014;104:2002–2009. doi:10.2105/AJPH.2013.301693)
dental care coverage for an older US population would increase dental care use only 1% to 8% after applying a nonparametric approach to account for errors in measuring self-reported dental care coverage and unobserved factors for aversion to risk and future dental care needs (B. Kreider, J. Pepper, R. Manski, and J. Moeller, unpublished data, 2012). In this study the increase in dental care use was significant but not as large or far-reaching as initially expected. Although the analyses confirmed that dental care coverage increases the likelihood of dental care use, the results also suggested that the effect of providing dental care coverage on use may be surprisingly lower than expected. We have provided empirical evidence and a theoretical model to help explain why the effect of dental care coverage on use may be less than expected and to more fully describe dental care use in relation to dental care coverage and other relevant determinants of use. Our findings may help research analysts, program developers, and policy planners better understand problems associated with policies and programs designed to encourage greater use of dental care among the population.
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METHODS We have proposed a comprehensive diagrammatic model (Figure 1) of the relationship between dental care use and multiple factors that are either modifiable or nonmodifiable for program planning or policy development.14 To develop our model we first began with and then adapted Andersen’s behavioral model of health services use as a theoretical framework to guide the development.15 Specifically, we considered several predisposing factors (age, gender, education, employment status, marital status, race/ethnicity, and health attitudes), enabling factors (income level and dental care coverage), and need factors (self-reported health status).15 We then extended our model to include market supply variables, environmental factors, and biomedical factors.16,17 We then made additional changes to our model to correspond with factor type and degree to which programs or policies could be developed to make an impact. For instance, we assembled factors into 2 broad groups: modifiable and nonmodifiable. Age, gender, and race/ethnicity are examples of nonmodifiable
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FIGURE 1—Comprehensive model of health services use: Health and Retirement Study, 2006–2008.
factors, and dental care coverage is an example of a modifiable factor because persons without coverage can receive coverage as a consequence of a new program or policy or can buy it on the market. Nonmodifiable factors are useful and important for purposes of program targeting and planning. We assembled all modifiable factors into groups to distinguish between preference, enabling, predisposing, need, market supply, environmental, and biomedical factors. Preference factors include knowledge, perception, tastes, attitudes, and beliefs. Enabling and predisposing factors include income, wealth, education, marital status, and family size. Need factors include overall health and oral health status. Market supply factors include numbers of dentists and dental hygienists.17 Less is known about environmental and biomedical factors, but we include
these in our model as a placeholder to guide future research.16 We used this reconfigured and expanded theoretical model to develop our regression models subject to data availability constraints in the HRS.
Data The HRS is a nationally representative longitudinal household survey administered by the Institute for Social Research at the University of Michigan and sponsored by the National Institute on Aging. It collects data every 2 years for individuals older than 50 years and their spouses.18,19 Specifically, we used the 2008 wave of the HRS, which contained 17 217 surveyed persons. The HRS is useful for the study of aging, retirement, and health among older US populations. It contains detailed information about
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demographics; income and assets; physical and mental health; cognition, family structure, and social supports; health care use and costs; health insurance coverage; labor force status and job history; and retirement planning and expectations. Each wave of the HRS collects self-reported information about medical and dental care use since the previous survey wave, approximately 2 years earlier.18,19 The HRS provides detailed information on numerous factors associated with dental care coverage and use, including out of pocket payments for dental visits. From the 2008 HRS sample of 17 217, we excluded 4250 respondents. Of these, we estimated that 1570 would not qualify for expanded Medicaid dental care coverage because they were aged 55 to 64 years, not on Medicare, and living in households with
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income greater than 133% of the poverty threshold. We used the income cutoff to identify Medicaid (below the cutoff) and Medicare (above the cutoff) recipients of expanded dental care coverage for those uninsured aged 55 years and older. We omitted another 1875 respondents because they had zero weights, as did the 166 who were younger than 55 years, an age group underrepresented in the HRS. The other 639 respondents we omitted represented less than 4% of the 2008 HRS sample and had missing data on at least 1 of the independent variables in our analysis. We then split our final sample of 12 967 respondents into 4 groups on the basis of their dental care coverage and dental care use as reported in the HRS and as illustrated in Table 1. Groups 1 and 2 had dental care coverage but only group 1 had dental care use. Groups 3 and 4 did not have dental care coverage, and only group 3 had dental care use.
health (self-reported health status, body mass index [BMI; defined as weight in kilograms divided by the square of height in meters], number of limitations in specific activities of daily living [ADLs], and number of chronic conditions ever diagnosed by a physician), and oral health status (all permanent teeth missing or not); and (2) nonmodifiable variables, including age, gender, and racial/ ethnic background. All independent variables are categorical except the numbers of ADLs and chronic conditions, which were continuous. We first estimated the relationship between group membership and each independent variable without adjusting for other covariates. Next, we estimated 3 multinomial logistic regressions for group membership distinguished by altering the reference groups for the regressions between groups 1 to 3 to produce the odds ratios (ORs) for the independent variables adjusted for other covariates in the models. This produced 6 unique comparisons: group 1 with each of the other 3 groups, group 2 with groups 3 and 4, and group 3 with group 4. We focused on comparing characteristics of persons with and those without dental care use holding dental care coverage constant, so we have shown only partial results from the multinomial logistic regressions comparing groups 1 and 2 (both with dental care coverage) and groups 3 and 4 (both without dental care coverage). In addition we have shown partial multinomial logistic regression results comparing persons in group 1 (those with both dental care coverage and use) to persons in group 4 (those lacking both coverage and use). The remaining 3 multinomial logistic results mainly contrast characteristics of those with and those without dental care coverage, holding dental care use constant (data available on request).
Analysis Our study was motivated by the relatively low dental care use rates estimated for persons without dental care coverage and use (group 4) under a hypothetical scenario in which they were assumed to have dental care coverage.14 This suggested that having dental care coverage per se was not sufficient to encourage older persons to use dental services at the same rate as those previously covered. This raised the question of what personal characteristics distinguished each of the 4 groups. To address this, the dependent variable in our study is group membership defined by the presence or absence of dental care use and dental care coverage. The independent variables included (1) modifiable variables, including household income relative to the poverty line, household wealth, education, marital status, region, retirement and labor force status, household size,
TABLE 1—Dental Insurance and Dental Care Use for Community-Based US Adults Aged 55 Years and Older: Health and Retirement Study, 2006–2008 Dental Coverage, No. Total Population, No.
Dental Use (Group 1)
No Dental Use (Group 2)
No Dental Coverage, No. Dental Use (Group 3)
No Dental Use (Group 4)
Sample Size
12 967
4337
1695
3809
3126
Weighted Sample (1000s)
60 370
25 188
7737
15 127
12 319
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The HRS core sample design is a multistage area probability sample of households, so we computed all estimates and statistics we have reported taking into account this design with the use of the software packages SUDAAN and Stata.20,21 We have discussed only estimates that were statistically significant at least at the 5% level.
RESULTS As shown in Table 2, group 1 respondents (those with dental care coverage and dental care use) were generally more likely than were each of the other 3 groups to be younger, college graduates, married, in excellent or very good health, not missing all their permanent teeth, high income, and in the labor force and not retired, and they were more likely to have fewer difficulties with ADLs and chronic conditions. They were also more likely than were persons without dental care use, whether with (group 2) or without (group 4) coverage, to be White non-Hispanic, living in households of 2 persons, in the highest household wealth decile, and living in the Midwestern or southern but not the western regions of the United States.
Persons With Dental Care Coverage and No Dental Care Use Group 2 respondents (those with dental care coverage but no use) were more likely than were groups 3 and 4 (those without any dental care coverage) to be younger, Black nonHispanics, living in a household with 3 or more persons and not alone, obese, and in the labor force and not retired. They were also more likely than were those in group 3 (persons without coverage but with use) to be male, Hispanic, high school but not college graduates, not in a 2-person household, in fair or poor health, missing all permanent teeth, poor and not high income, in the lowest wealth deciles, and not partly retired, and they were more likely to have a greater number of difficulties with ADLs. They were also more likely than were persons with no coverage and no dental care use (group 4) to be college graduates and not high school graduates, married, in excellent or very good health, not missing all their permanent teeth, high income and not poor or low income, and not in the labor force and not retired, and
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TABLE 2—Population Characteristics of Groups of US Civilian, Noninstitutionalized Persons Defined by Dental Care Coverage and Dental Care Use: Health and Retirement Study, 2006–2008 Dental Care Coverage
Population Characteristics
No Dental Care Coverage
Population Size (1000s)
Total (n = 60 370), Mean (SD)
Group 1: Dental Care Use (n = 25 188), Mean (SD)
Group 2: No Dental Care Use (n = 7737), Mean (SD)
Group 3: Dental Care Use (n = 15 127), Mean (SD)
Group 4: No Dental Care Use (n = 12 319), Mean (SD)
23 304
38.15 (0.67)
63.20e (1.06)
48.19f (1.83)
7.44g (0.76)
18.36 (1.26)
e
f
Age, y 55–64 65–69
11 737
19.44 (0.45)
15.40 (0.63)
17.63 (1.17)
25.20g (1.03)
21.76 (1.00)
70–74
9493
15.73 (0.31)
9.42e (0.46)
13.61f (0.78)
23.74g (0.77)
20.09 (0.76)
75–79
6864
11.37 (0.33)
5.91e (0.35)
9.00f (0.64)
18.71g (0.58)
15.00 (0.68)
80–84
4612
7.64 (0.35)
3.39e (0.17)
6.16f (0.49)
12.37 (0.70)
11.44 (0.76)
4630 33 702
7.67 (0.34) 55.83 (0.46)
2.67e (0.25) 55.53d (0.71)
5.40f (0.64) 53.02d (1.76)
12.53 (0.76) 58.37g (0.82)
13.34 (0.76) 55.07 (0.92)
5507
9.12 (0.48)
7.89e (0.65)
19.38f (1.14)
3.68g (0.33)
11.88 (0.85)
‡ 85 Gender: female Race Black non-Hispanic
3769
6.24 (0.78)
5.30 (0.50)
9.86 (1.50)
4.06g (0.59)
8.59 (1.75)
White non-Hispanic
49 659
82.26 (0.96)
84.24e (0.87)
66.99f (2.03)
90.94g (0.68)
77.14 (1.87)
Other non-Hispanic
1435
2.38 (0.32)
2.58d (0.34)
3.77 (1.09)
1.32g (0.22)
2.40 (0.47)
Some or no school High school graduate
35 301 10 544
58.47 (0.75) 17.47 (0.67)
57.47d (1.09) 7.04e (0.40)
57.47 (1.91) 30.53f (1.77)
61.49g (0.99) 13.58g (0.62)
57.46 (1.38) 35.35 (1.42)
College graduate
14 525
24.06 (0.84)
35.49e (1.23)
12.00f (1.16)
24.94g (1.12)
7.19 (0.63)
37 158
61.55 (0.73)
71.17e (0.86)
55.56g (1.86)
59.36g (1.08)
48.34 (1.28)
2344
3.88 (0.22)
4.02 (0.32)
4.52 (0.65)
3.60 (0.40)
3.55 (0.43)
20 868
34.57 (0.67)
24.81e (0.83)
39.92g (1.60)
37.05g (0.99)
48.11 (1.17)
1 2
15 889 32 042
26.32 (0.51) 53.08 (0.80)
20.59e (0.66) 55.94c (1.11)
25.07f (1.32) 44.76d (1.59)
30.95 (0.98) 57.54g (1.12)
33.13 (1.28) 46.96 (1.56)
‡3
12 440
20.61 (0.69)
23.47e (0.91)
30.17f (1.83)
11.50g (0.83)
19.91 (1.10)
24 190
40.07 (0.76)
50.68e (0.99)
31.05f (1.33)
40.59g (1.13)
23.41 (1.00)
Hispanic
c
d
Education
Marital status Married Widowed or divorced Never married Household size, No. persons
Health status Excellent or very good Good
19 167
31.75 (0.65)
31.64 (0.95)
29.55 (1.28)
34.46g (0.95)
30.02 (1.33)
Fair or poor
17 014
28.18 (0.66)
17.68e (0.64)
39.39f (1.51)
24.95g (0.70)
46.57 (1.22)
9749
16.15 (0.51)
3.00e (0.26)
35.61f (1.49)
5.88g (0.37)
43.41 (1.17)
978
1.62 (0.52)
0.87f (0.21)
1.42 (0.42)
2.45 (0.32)
2.27 (0.35)
Normal
17 300
28.66 (0.52)
26.91e (0.72)
22.41f (1.25)
34.14g (1.05)
29.42 (0.94)
Overweight
23 100
38.26 (0.50)
39.46g (0.86)
36.36 (1.54)
38.84 (0.99)
36.30 (1.06)
Obese
18 992
31.46 (0.58)
32.76b (0.82)
39.81f (1.42)
24.58g (0.97)
32.00 (1.04)
5676
9.40 (0.57)
4.53e (0.41)
14.66f (1.54)
6.96g (0.62)
19.07 (1.30)
All permanent teeth missing Body mass index Underweight
d
d
Family incomea Poor Low Middle High
e
g
9977
16.53 (0.41)
7.47 (0.50)
20.15 (1.09)
19.51g (0.86)
29.10 (1.04)
17 321 27 396
28.69 (0.55) 45.38 (0.95)
23.41e (0.84) 64.59e (1.25)
31.49 (1.40) 33.70f (1.75)
33.40 (0.82) 40.13g (1.25)
31.94 (1.01) 19.89 (0.88) Continued
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RESEARCH AND PRACTICE
TABLE 2—Continued Household wealth, decile 1–3
16 576
27.46 (0.86)
19.11e (0.92)
48.45d (1.72)
16.23g (0.85)
45.12 (1.42)
d
4–6
18 431
30.53 (0.73)
30.44 (1.16)
32.42 (1.37)
28.23g (1.31)
32.35 (1.04)
7–9
19 330
32.02 (0.99)
39.05c (1.33)
16.39d (1.24)
39.26g (1.32)
18.57 (1.18)
6034
9.99 (0.52)
11.40e (0.74)
2.74d (0.43)
16.29g (1.01)
3.95 (0.45)
15 758
26.10 (1.66)
27.85c (2.17)
21.95d (1.97)
28.83g (2.42)
21.79 (1.62)
Northeast
10 525
17.43 (1.42)
17.87 (1.68)
16.23 (1.53)
17.79 (1.84)
16.82 (1.93)
South
11 960
19.81 (1.61)
23.06c (2.07)
17.10 (1.71)
19.74 (1.84)
14.97 (1.69)
West
22 128
36.65 (1.41)
31.21c (1.61)
44.72d (1.95)
33.64g (2.02)
46.42 (2.14)
31 755
52.60 (0.73)
35.83e (1.04)
49.92f (1.59)
68.25 (1.02)
69.35 (1.31)
6182
10.24 (0.31)
e
10.38 (0.51)
7.79d (0.73)
12.94g (0.73)
8.18 (0.68)
16 099 6335
26.67 (0.66) 10.49 (0.41)
45.87e (1.31) 7.92e (0.51)
30.89f (1.88) 11.41g (0.96)
7.95 (0.55) 10.86g (0.62)
7.74 (0.71) 14.73 (0.84)
NA
0.31 (0.01)
0.18e (0.01)
0.42f (0.03)
0.31g (0.02)
0.54 (0.02)
2.39g (0.03)
2.77 (0.04)
10 Region Midwest
Retirement or labor force status Fully retired Partly retired In the labor force, not retired Not in the labor force, not retired No. of difficulties with activities of daily living No. of chronic conditions
NA
2.27 (0.02)
e
1.89 (0.03)
f
2.47 (0.05)
Note. NA = not applicable. Table is derived from a sample of 12 967 persons from the 2008 Health and Retirement Study who were aged 55 years and older with positive-valued weights and without any missing data. Rounding accounts for any column sums not equal to totals. a Where low income refers to persons in families with incomes 101%–199% of the poverty line; middle income, 201%–400% of the poverty line; and high income, > 400% of the poverty line. Poor persons are at or below 100% of the poverty line, including persons in families with negative income. b Indicates that the mean in the column is significantly different from the mean of those with dental care coverage and no dental care use and the mean of those with dental care use and no dental care coverage (P £ .05). c Indicates that the mean in the column is significantly different from the mean of those with dental care coverage and no dental care use and the mean of those with no dental care coverage and no dental care use (P £ .05). d Indicates that the mean in the column is significantly different from the mean of those with dental care use and no dental care coverage (P £ .05). e Indicates that the mean in the column is significantly different from the mean in each of the other 3 columns (P £.05). f Indicates that the mean in the column is significantly different from the mean in each of the last 2 columns (P £ .05). g Indicates that the mean in the column is significantly different from the mean in the last column (P £ .05).
they were more likely to have had fewer difficulties with ADLs and chronic conditions.
Persons With Dental Care Use and No Dental Care Coverage Group 3 respondents (those with dental care use and no dental care coverage) were more likely than were group 4 respondents (those without dental care coverage and use) to be aged 65 to 79 years and not younger than 65 years; female; White non-Hispanic and not minorities; college graduates; married; living in households of 2 persons and not 3 persons or more; in good, very good, or excellent health; not missing all permanent teeth; normal BMI and not obese; high income and not poor or low income; in the higher wealth deciles; living in the Midwest and not in the western regions of the United States; and partly retired. They were also more likely to have fewer difficulties with ADLs and chronic conditions.
Dental Care Use As shown in Table 3, the odds of not using dental care relative to using dental care, holding coverage constant (group 2 vs group 1 and group 4 vs group 3), were higher for men than for women, Black non-Hispanics than White non-Hispanics, noncollege graduates than college graduates, never married than married, in fair or poor health than in excellent or very good health, missing all permanent teeth than not missing all permanent teeth, not high income, in the sixth or lower household wealth deciles than in the highest wealth decile, and living in the western than the southern region of the United States. For those with coverage (group 2 vs group 1), the likelihood of not using dental care was higher for those aged 65 years and older than for those younger than 65 years, for those in 3 or more person households than for those living alone, and for those obese than for those with normal BMI values.
2006 | Research and Practice | Peer Reviewed | Manski et al.
Interestingly, for those without coverage (group 4 vs group 3), the odds of not using dental care were lower for persons aged 65 years and older than for those younger than 65 years. In neither case was dental care use correlated with retirement and labor force status and the number of chronic conditions and difficulties with ADLs.
Dental Care Coverage and Use Respondents without coverage and use (group 4) have attributes highly correlated with nonuse relative to respondents with both (group 1), such as being much older, male, less educated, never married, less healthy, missing all permanent teeth, less income and wealth, and being in the West rather than the South (Table 2). White non-Hispanics have a higher likelihood than do Black non-Hispanics of being without dental care coverage and use than of having both. Similarly, persons with lower
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TABLE 3—Adjusted Odds Ratios from Multinomial Logistic Regressions Comparing US Participant Variables of Interest by Use and Insurance Coverage of Dental Care: Health and Retirement Study, 2006–2008 Group 2a vs Group 1,b OR (95% CI)c
Group 4d vs Group 3,e OR (95% CI)f
Group 4d vs Group 1,b OR (95% CI)c
55–64 (Ref) 65–69
1.00 1.51** (1.17, 1.95)
1.00 0.39** (0.28, 0.52)
1.00 4.64** (3.82, 5.62)
70–74
1.66** (1.30, 2.12)
0.35** (0.25, 0.47)
5.55** (4.49, 6.85)
75–79
1.64** (1.21, 2.23)
0.31** (0.23, 0.43)
5.72** (4.67 7.01)
80–84
1.84** (1.30, 2.59)
0.35** (0.25, 0.48)
6.70** (5.11, 8.79)
‡ 85
1.87** (1.24, 2.82)
0.37** (0.26, 0.53)
8.45** (6.11, 11.68)
1.56** (1.32, 1.85)
1.52** (1.31, 1.77)
1.69** (1.48, 1.93)
Population Characteristics Age, y
Gender Male Female (Ref) Race Black non-Hispanic
1.00
1.00
1.00
1.46** (1.14, 1.87)
1.72** (1.40, 2.12)
0.71** (0.57, 0.88)
Hispanic
0.95 (0.68, 1.34)
0.96 (0.67, 1.37)
0.70g (0.46, 1.06)
White non-Hispanic (Ref)
1.00
1.00
1.00
Other non-Hispanic
1.55 (0.90, 2.68)
1.50g (0.93, 2.42)
0.93 (0.59, 1.46)
Education Some or no school
1.59** (1.23, 2.05)
1.83** (1.54, 2.19)
2.31** (1.90, 2.81)
High school graduate College graduate (Ref)
2.89** (2.16, 3.86) 1.00
2.76** (2.22, 3.43) 1.00
3.54** (2.68, 4.67) 1.00
Marital status Married (Ref)
1.00
1.00
1.00
Widowed or divorced
1.52g (0.96, 2.40)
1.09 (0.69, 1.73)
1.42g (0.96, 2.10)
1.64** (1.20, 2.26)
1.46** (1.13, 1.89)
1.64* (1.26, 2.13)
Never married Household size, no. 1 (Ref) 2 ‡3
1.00 1.21 (0.85, 1.74) 1.36* (1.00, 1.86)
1.00
1.00
1.09 (0.81, 1.48) 1.36g (0.99, 1.85)
1.30g (0.98, 1.72) 1.14 (0.86, 1.52)
Health status Excellent or very good (Ref)
1.00
1.00
Good
1.01 (0.83, 1.24)
1.12 (0.95, 1.34)
1.33** (1.11, 1.60)
1.50** (1.26, 1.78)
1.59** (1.33, 1.91)
2.18** (1.86, 2.56)
11.60** (9.13, 14.73)
10.50** (9.06, 12.19)
10.38** (8.69, 12.41)
Fair or poor
1.00
Permanent teeth All missing Not all missing (Ref) Body mass index
1.00
1.00
Underweight
1.23 (0.57, 2.64)
0.61g (0.37, 1.02)
1.38 (0.73, 2.64)
Normal (Ref)
1.00
1.00
1.00
Overweight
1.19g (0.97, 1.41)
1.09 (0.93, 1.28)
1.08 (0.91, 1.27)
1.35** (1.10, 1.67)
1.16g (1.00, 1.36)
1.10 (0.94, 1.28)
Poor
2.29** (1.57, 3.32)
1.49* (1.02, 2.03)
6.02** (4.26, 8.51)
Low Middle
1.83** (1.39, 2.41) 1.35** (1.09, 1.67)
1.28* (1.04, 1.57) 1.26** (1.09, 1.47)
3.45** (2.68, 4.44) 1.91** (1.63, 2.23)
Obese
1.00
Household incomeh
High (Ref)
1.00
1.00
1.00 Continued
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numbers of chronic conditions are more likely than are those with higher numbers of being without dental care coverage and use than of having both. Being out of the labor force raises the likelihood of having neither use nor coverage compared with having both but only because coverage was not held constant in making this comparison. When coverage is controlled, there is no correlation between labor force and retirement status and dental care use.
DISCUSSION Policy planners and dental care advocates often look to users of dental care, and especially users of dental care with dental care coverage, to benchmark goals and objectives for programs designed to improve access among nonusers. Useful for establishing aspirational program targets, these goals and objectives are often not fully realized (B. Kreider, J. Pepper, R. Manski, and J. Moeller, unpublished data).13 Our results show that dental care users with dental care coverage differ from dental care nonusers without dental care coverage in ways that are important to consider when developing policy. More importantly, many of these differences persist even when controlling for dental care coverage. Persistence of nonuse, independent of dental care coverage status, suggests that attempts to improve access by focusing only on the provision of dental care coverage might be ineffective. Having established personal attributes correlated with dental care use better enables an understanding of why uninsured older US adults not using dental care are not likely to have high use rates even if they did obtain dental care coverage. The policy implications of these findings are important. Providing dental care coverage to an older population previously using dental care primarily reshuffles payment sources and may induce additional demand for services. More importantly, providing dental care coverage to an uninsured older population previously without dental care use may encourage them to seek dental services. Generally, a conventional understanding among health policy planners is that providing dental care coverage to this latter uninsured population would result in dental use estimates
Manski et al. | Peer Reviewed | Research and Practice | 2007
RESEARCH AND PRACTICE
TABLE 3—Continued Household wealth, decile 1–3
4.21** (2.63, 6.74)
3.98** (2.99, 5.29)
2.54** (1.89, 3.40)
4–6
2.48** (1.64, 3.74)
2.38** (1.82, 3.10)
1.51** (1.12, 2.20)
7–9
1.29 (0.82, 2.20)
1.39* (1.03, 1.88)
10 (Ref)
1.00
1.00
1.00 1.09 (0.82, 1.45)
Region Midwest
0.90 (0.65, 1.25)
0.93 (0.71, 1.22)
0.81 (0.62, 1.05)
Northeast
1.06 (0.78, 1.45)
0.98 (0.77, 1.25)
1.17 (0.84, 1.64)
South (Ref)
1.00
1.00
1.00
West
1.47** (1.14, 1.90)
1.32** (1.08, 1.62)
2.05** (1.51, 2.78)
Retirement or labor force status Fully retired
0.97 (0.72, 1.30)
1.05 (0.80, 1.38)
2.30** (1.80, 2.94)
Partly retired
0.87 (0.63, 1.21)
0.88 (0.62, 1.24)
1.98** (1.42, 2.77)
In the labor force, not retired (Ref) Not in the labor force, not retired
1.00 0.78 (0.55, 1.09)
1.00 0.93 (0.70, 1.24)
1.00 2.14** (1.60, 2.85)
1.00 (0.90, 1.11)
0.99 (0.90, 1.08)
1.01 (0.93, 1.10)
0.96 (0.91, 1.02)
0.96 (0.91, 1.02)
0.93* (0.87, 0.99)
No. of difficulties with activities of daily living No. of chronic conditions
Note. CI = confidence interval; OR = odds ratio. The multinomial logistic regression contains a sample of 12 967 persons from the 2008 Health and Retirement Study who were aged 55 years and older, with positive-valued weights and without any missing data. For continuous covariates the OR point estimate is derived from a 1-unit change in the variable. The adjusted value refers to the inclusion in the regression of the control variables listed in the rows of the table. a Group 1 consists of dental care coverage and use. b Group 2 consists of dental care coverage and no dental care use. c Estimated from a multinomial logistic equation with group 1 (dental care coverage and use) as the reference group. d Group 4 consists of no dental care coverage and no dental care use. e Group 3 consists of dental care use and no dental care coverage. f Estimated from a multinomial logistic equation with group 3 (dental care use and no dental care coverage) as the reference group. g Approached statistical significance at P < .1. h Where low income refers to persons in families with incomes 101%–199% of the poverty line; middle income, 201%–400% of the poverty line; and high income, > 400% of the poverty line. Poor persons are at or below 100% of the poverty line, including persons in families with negative income. *P £ .05; **P £ .01.
similar if not identical to those of the previously covered population. Referred to by economists as the counterfactual, the group receiving a treatment is often different from the control group in many ways.22 Our targeted control group was persons with dental care coverage and dental care use. We targeted dental care coverage to uninsured persons without dental care use who have lower income, less wealth, lower educational levels, worse health, and higher age than does the targeted control group. It is likely that the targeted treatment group differs from the control group in attitude and preference for oral health. In fact, previous work by Cooper et al.13 showed that preference factors can and do modulate the effect on use of having coverage (B. Kreider, J. Pepper, R. Manski, and
J. Moeller, unpublished data). As such, social determinants of health should be included as a measure complementary to dental care coverage in efforts to improve dental care use in older US adults. In addition, if we assume that the dental care coverage and use market is stable and in economic equilibrium, providing dental care coverage to persons previously uninsured will disrupt that equilibrium and produce inefficiencies in achieving short-term marginal increases in use. Both demand-side and supply-side factors could result in less use than expected. For instance, expanded dental insurance might not increase use unless coverage reduced the cost of dental care to a level that was viewed as affordable. Also, if the dental care supply is fixed in the short term,
2008 | Research and Practice | Peer Reviewed | Manski et al.
appointments may be delayed or not available until capacity can be increased. Therefore, our findings are not surprising, and if the goal of providing coverage is to improve access among current nonusers, policy planners should contemplate including programs to increase the awareness and preference for dental care among nonusers and include programs to mitigate any disruption of the market equilibrium.
Model Application The value of this adaptation and approach is the capacity of this model to guide the development of new programs and policy. Accordingly, if the goal is to improve access by adding dental care coverage and thereby stimulate the use of dental services among current nonusers, policy planners should contemplate including programs to increase the overall acceptance, participation, and use of newly offered dental care coverage plans among eligible participants. Policy planners might wish to consider the addition of program elements designed to positively stimulate modifying factors by introducing a health promotion and literacy program that will improve the understanding of coverage and create a more positive outlook about dental care among modifiable preference variables such as knowledge, perception, tastes, attitudes, and beliefs. Furthermore, our findings support the inclusion of market supply factors in our model. For instance, the persistence of the western region influence showing lower use in that part of the country than other regions could proxy for market supply factors, such as a lower number of dentist or dental hygiene providers per capita. To mitigate any negative effect of supply-side variables, dental and dental hygiene school programs could be offered inducements to consider expanded and accelerated programs and debt forgiveness to provide workforce market stability during a period of increasing demand. Simply, recognizing the role of the supply-side variables and modifying factors makes the successful introduction of a plan to improve access and a plan that is more likely to approximate the prereform target goals more likely. Enabling and predisposing variables such as income, wealth, education, marital status, and family size are modifiable but only over
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a longer term. Policies and programs will take some time to implement and perhaps longer to have an effect. Understanding the implications of the time element during the program development could allow planners to better schedule, coordinate, and phase in new policies. Furthermore, our analyses showed that there is no correlation between employment status and dental care use when controlling for dental care coverage. Therefore, we treat coverage and employment status factors as interdependent, unique, and distinct from other enabling factors. In our model we posit that health and oral health status are also interdependent and are a function of preference variables, environmental variables, biomedical variables, and cointerdependent with use itself.16 Less is known about environmental and biomedical factors.16 Additional study and research is warranted to better understand the role and relationship of these factors, which may result in new opportunities to develop relevant and worthwhile interventions.
Conclusions Our findings show that the personal attributes of older US adults without dental care coverage and dental care use differ from those of older US adults with both use and coverage. This suggests that providing dental care coverage to uninsured older US adults without use will not necessarily result in higher use rates and likely will not result in use rates similar to those with prior coverage and use. We have offered a model and an approach showing how program developers and policy planners can consider modifiable factors as they plan for and attempt to implement dental care coverage extensions to persons previously uninsured. The use of this approach would make coverage uptake and use more likely and increase the likelihood that nonusers without coverage postextension would approximate or approach the use rates of those with prior use and coverage. j
About the Authors Richard J. Manski, John F. Moeller, and Haiyan Chen are with Dental Public Health, University of Maryland School of Dentistry, Baltimore. Correspondence should be sent to Richard J. Manski, DDS, MBA, PhD, Professor, Dental Public Health, University of Maryland School of Dentistry, 650 West Baltimore Street, Room 2209, Baltimore, MD 21201
(e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted September 20, 2013.
13. Cooper PF, Manski RJ, Pepper JV. The effect of dental insurance on dental care use and selection bias. Med Care. 2012;50(9):757---763.
Contributors
14. Manski RJ, Moeller JR, Chen H, et al. Dental care utilization and retirement. J Public Health Dent. 2010; 70(1):67---75.
R. J. Manski conceptualized and supervised the study and served as content expert on dental care. J. F. Moeller supervised the analyses and assisted with the writing of the article. H. Chen assisted with analyses.
Acknowledgments This investigation (Dental Coverage Transitions, Utilization and Retirement) was supported by the National Institute of Dental and Craniofacial Research of the National Institutes of Health (grant R01DE021678). The Health and Retirement Study is sponsored by the National Institute of Aging (grant NIA U01AG009740) and is conducted by the University of Michigan.
Human Participant Protection No protocol approval was necessary because public data were obtained from secondary sources.
15. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1---10. 16. Diez Roux AV. Integrating social and biologic factors in health research: a systems view. Ann Epidemiol. 2007;17(7):569---574. 17. Murphya GT, Kephart G, Lethbridgea L, O’BrienPallasc L, Birch S. Planning for what? Challenging the assumptions of health human resources planning. Health Policy. 2009;92(2---3):225---233. 18. RAND Center for the Study of Aging, Labor, and Population. RAND HRS Data, Version H. Santa Monica, CA; 2008. 19. St. Clair P, Blake D, Bugliari D. RAND HRS Data Documentation, Version J. Santa Monica, CA; 2010. 20. SUDAAN, Release 6.40. [computer program]. Research Triangle Park, NC: Research Triangle Institute; 1995.
References 1. Brown E, Manski R. Dental Services: Use, Expenses, and Sources of Payment, 1996---2000. Rockville, MD: Agency for Healthcare Research and Quality; 2004. AHRQ Pub. 04---0018. 2. Manski RJ, Brown E. Dental Use, Expenses, Private Dental Coverage, and Changes, 1996 and 2004. Rockville, MD: Agency for Healthcare Research and Quality; 2007.
21. Stata Statistical Software, Version 7.0. [computer program]. College Station, TX: StataCorp LP; 2001. 22. Manski CF. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press; 1995.
3. National Association of Dental Plans; Delta Dental Plans Association. Offering Dental Benefits in Health Exchanges: A Roadmap for Federal and State Policymakers. Dallas, TX: NADP/DDPA; 2011. NADP/DDPA white paper. 4. Manski RJ, Cooper PF. Dental care use: does dental insurance truly make a difference in the US? Community Dent Health. 2007;24(4):205---212. 5. Manning WG, Bailit HL, Benjamin B, Newhouse JP. The demand for dental care: evidence from a randomized trial in health insurance. J Am Dent Assoc. 1985; 110(6):895---902. 6. Manning WG, Phelps CE. The demand for dental care. Bell J Econ. 1979;10:503---525. 7. Mueller CD, Monheit AC. Insurance coverage and the demand for dental care. Results for non-aged White adults. J Health Econ. 1988;7(1):59---72. 8. US Department of Health and Human Services. Oral Health in America: A Report of the Surgeon General. Rockville, MD: National Institute of Dental and Craniofacial Research; 2000. 9. Institute of Medicine. Advancing Oral Health in America. Washington, DC: National Academies Press; 2011. 10. Institute of Medicine; National Research Council. Improving Access to Oral Health Care for Vulnerable and Underserved Populations. Washington, DC: National Academies Press; 2011. 11. US General Accounting Office. Factors Contributing to Low Use of Dental Services by Low-Income Populations. Washington, DC; 2000. GAO/HEHS-00---149. 12. US General Accounting Office. Dental Disease Is a Chronic Problem Among Low-Income Populations. Washington, DC; 2000. GAO/HEHS-00---72.
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